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Impact of the COVID-19 induced lockdown measures on PM2.5 concentration in USA.

ABSTRACT: In 2020, most countries around the world have observed varying degrees of public lockdown measures to mitigate the transmission of SARS-CoV-2. As an unintended consequence of reduced transportation and industrial activities, air quality has dramatically improved in many major cities around the world. In this paper, we analyze the environmental impact of the lockdown measures on PM2.5 concentration levels in 48 core-based statistical areas (CBSA) of the United States, during the pre and post-lockdown period of January to June 2020. We model the effect of lockdown on the PM2.5 concentration in different CBSAs while adjusting for various meteorological factors like temperature, wind-speed, precipitation and snow. Linear mixed effects models and functional regression methods with random intercepts are employed to capture the heterogeneity of the effect across different regions. Our analysis shows there is a statistically significant reduction in levels of PM2.5 across most of the regions during the lock-down period, although interestingly, this effect is not uniform across all the CBSAs under consideration.

SUBMITTER: Ghosal R

PROVIDER: S-EPMC8025541 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature

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Impact of the COVID-19 induced lockdown measures on P M 2.5 concentration in USA.

Ghosal Rahul R Saha Enakshi E

Atmospheric environment (Oxford, England : 1994) 20210407

In 2020, most countries around the world have observed varying degrees of public lockdown measures to mitigate the transmission of SARS-CoV-2. As an unintended consequence of reduced transportation and industrial activities, air quality has dramatically improved in many major cities around the world. In this paper, we analyze the environmental impact of the lockdown measures on P M 2.5 concentration levels in 48 core-based statistical areas (CBSA) of the United States, during the pre and post- ...[more]

PMID: 33841026

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Project description:Stringent lockdown measures implemented in Italy to mitigate the spread of COVID-19 are generating unprecedented economic impacts. However, the environmental consequences associated with the temporary shutdown and recovery of industrial and commercial activities are still not fully understood. Using the well-known carbon footprint (CF) indicator, this paper provides a comprehensive estimation of environmental effects due to the COVID-19 outbreak lockdown measures in Italy. Our aim was to quantify the CF associated with the consumption of energy by any economic activity and region in Italy during the lockdown, and then compare these environmental burdens with the CF calculated for analogous periods from 2015 to 2019 (~March and April). Complementarily, we also conducted a scenario analysis to estimate the post-lockdown CF impact in Italy. A consumption-based approach was applied according to the principles of the established Life Cycle Assessment method. The CF was therefore quantified as a sum of direct and indirect greenhouse gases (GHGs) released from domestically produced and imported energy metabolism flows, excluding the exports. Our findings indicate that the CF in the lockdown period is ~-20% lower than the mean CF calculated for the past. This means avoided GHGs in between ~5.6 and ~10.6 Mt CO2e. Results further suggest that a tendency occurs towards higher impact savings in the Northern regions, on average ~230 kt CO2e of GHGs avoided by province (against ~110-130 kt CO2e in central and Southern provinces). Not surprisingly, these are the utmost industrialized areas of Italy and have been the ones mostly affected by the outbreak. Despite our CF estimates are not free of uncertainties, our research offers quantitative insights to start understanding the magnitude generated by such an exceptional lockdown event in Italy on climate change, and to complement current scientific efforts investigating the relationships between air pollution and the spread of COVID-19.

Project description:The impacts of the meteorological condition and emissions reduction on the aerosol concentration over the Beijing–Tianjin–Hebei (BTH) region during the COVID-19 lockdown were analyzed by conducting three numerical experiments, including one with the meteorological field in 2019 and MEIC-2019 (2019 monthly Multi-resolution Emissions Inventory for China), one with the meteorological field in 2020 and MEIC-2019, and one with the meteorological field in 2020 and MEIC-2020, via a WRF-Chem model. The numerical experiments were performed from 3 to 16 February in 2019 and in 2020, during which a severe fog–haze event (3–16 February 2020) occurred in the BTH region, with a simulated maximum daily PM2.5 of 245 μg m−3 in Tangshan and 175 μg m−3 in Beijing. The results indicate that the daily PM2.5 decreased by 5–150 μg m−3 due to the emissions reduction and increased by 10–175 μg m−3 due to the meteorological condition in Beijing, Shijiazhuang, Cangzhou, Handan, Hengshui, Chengde, Zhangjiakou, and Tangshan from 7 to 14 February. For the horizontal distribution, PM2.5 and different aerosol species concentrations from 7 to 14 February 2020 increased compared with those during the same period in 2019, indicating that the accumulation of pollutants caused by the unfavorable meteorological condition offset the decreases caused by the emissions reduction, leading to the high aerosol concentration during the COVID-19 lockdown. 摘要本研究从气象条件和减排两方面探讨了COVID-19封锁期间严重雾霾发生的原因[研究目的], 并定量的分析了气象条件和减排对PM2.5浓度的相对影响[创新点].使用WRF-Chem在2019年2月3日–16日和2020年2月3日–16日进行了三个数值模拟试验, 分别为2019年气象场与MEIC-2019 (2019年中国多分辨率月度排放清单) ,2020年气象场与MEIC-2019,2020年气象场与MEIC-2020[研究方法].结果表明COVID-19封锁期间严重雾霾的原因是不利气象条件增加的PM2.5浓度大于减排减少的PM2.5浓度[重要结论]. Graphical abstract Image, graphical abstract

Project description:BackgroundDuring COVID-19 lockdown measures, memory clinic patients reported worries for faster cognitive decline, due to loss of structure and feelings of loneliness and depression. We aimed to investigate the impact of the COVID-19 lockdown on rate of cognitive decline in a mixed memory clinic population, compared to matched historical controls.MethodsWe included patients who visited Alzheimer Center Amsterdam 6 months to 1 week before the first Dutch COVID-19 lockdown, and had a second visit 1 year later, after this lockdown period (n = 113; 66 ± 7 years old; 30% female; n = 55 dementia, n = 31 mild cognitive impairment (MCI), n = 18 subjective cognitive decline (SCD), n = 9 postponed diagnosis). Historical controls (visit in 2016/2017 and second visit 1 year later (n = 640)) were matched 1:1 to lockdown patients by optimal Mahalanobis distance matching (both groups n = 113). Groups were well matched. Differences between lockdown patients and historical controls over time in Mini-Mental State Examination, Trail Making Test part A and B, Rey-Auditory Verbal Learning Test (RAVLT) immediate and delayed recall, and category fluency scores were analyzed using linear mixed effect models with random intercepts. We examined differences in rate of cognitive decline between whole groups, and after stratification in SCD, MCI, and dementia separately.ResultsLockdown patients had a faster rate of memory decline compared to controls on both RAVLT immediate [B(SE) = - 2.62 (1.07), p = 0.015] and delayed recall [B(SE) = - 1.07 (0.34), p = 0.002]. Stratification by syndrome diagnosis showed that this effect was largely attributable to non-demented participants, as we observed faster memory decline during lockdown in SCD and MCI (RAVLT immediate [SCD: B(SE) = - 6.85 (2.97), p = 0.027; MCI: B(SE) = - 6.14 (1.78), p = 0.001] and delayed recall [SCD: B(SE) = - 2.45 (1.11), p = 0.035; MCI: B(SE) = - 1.50 (0.51), p = 0.005]), but not in dementia.ConclusionMemory clinic patients, specifically in pre-dementia stages, showed faster memory decline during COVID-19 lockdown, providing evidence that lockdown regulations had a deleterious effect on brain health. In individuals that may have been able to deal with accumulating, subclinical neuropathology under normal and structured circumstances, the additional stress of lockdown regulations may have acted as a "second hit," resulting in less beneficial disease trajectory.

Project description:BackgroundHow effective have lockdowns been at reducing the covid-19 infection and mortality rates? Lockdowns influence contact among persons within or between populations including restricting travel, closing schools, prohibiting public gatherings, requiring workplace closures, all designed to slow the contagion of the virus. The purpose of the present study was to assess the impact of lockdown measures on the spread of covid-19 and test a theoretical model of the covid-19 pandemic employing structural equation modelling.MethodsLockdown variables, population demographics, mortality rates, infection rates, and health were obtained for eight countries: Austria, Belgium, France, Germany, Italy, Netherlands, Spain, and the United Kingdom. The dataset, owid-covid-data.csv, was downloaded on 06/01/2020 from: https://github.com/owid/covid-19-data/tree/master/public/data. Infection spread and mortality data were depicted as logistic growth and analyzed with stepwise multiple regression. The overall structure of the covid-19 data was explored through factor analyses leading to a theoretical model that was tested using latent variable path analysis.ResultsMultiple regression indicated that the time from lockdown had a small but significant effect (β = 0.112, p< 0.01) on reducing the number of cases per million. The stringency index produced the most important effect for mortality and infection rates (β = 0.588,β = 0.702, β = 0.518, β = 0.681; p< 0.01). Exploratory and confirmatory analyses resulted in meaningful and cohesive latent variables: 1) Mortality, 2) Infection Spread, 3) Pop Health Risk, and 4) Health Vulnerability (Comparative Fit Index = 0.91; Standardized Root Mean Square Residual = 0.08).DiscussionThe stringency index had a large impact on the growth of covid-19 infection and mortality rates as did percentage of population aged over 65, median age, per capita GDP, diabetes prevalence, cardiovascular death rates, and ICU hospital beds per 100K. The overall Latent Variable Path Analysis is theoretically meaningful and coherent with acceptable fit indices as a model of the covid-19 pandemic.

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Impact of the COVID-19 induced lockdown measures on PM2.5 concentration in USA.

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Impact of the COVID-19 induced lockdown measures on P M 2.5 concentration in USA.

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